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CLEAR CARE PLUS Cleaning & Disinfecting Solution is indicated for use in simultaneous cleaning, daily protein removal, disinfection, and storing of soft (hydrophilic) contact lenses (including silicone hydrogel lenses) and rigid gas permeable (fluoro silicone acrylate and silicone acrylate) contact lenses, as recommended by your eye care professional.

FID 120947A is a sterile solution containing micro-filtered hydrogen peroxide 3% as the active ingredient. The formulation contains dual surfactants, Pluronic® 17R4 and a novel block copolymer surfactant, EO10BO3. composed of ethylene oxide and butylene oxide (nEOBO), to enhance wettability of contact lens surfaces. The formulation also contains phosphonic acid, a metal chelating agent to stabilize hydrogen peroxide, sodium phosphates as buffering agents, and sodium chloride as a tonicity agent. The sterile solution is aseptically filled and packaged in sterilized plastic bottles [gamma irradiated bottle; ethylene oxide (EtO) snap cap) with a tamper-evident seal and labeling to include the manufacturing lot number and expiration date. A special lens case, consisting of a transparent cup and connected unit of screw cap, lens holders (baskets) and platinum-coated neutralizing disc, is provided with each purchase of FID 120947A. The lens case is the same as that provided with the previously cleared device.

This document is a 510(k) summary for a contact lens cleaning and disinfecting solution, not an AI/ML device. Therefore, it does not contain the information required to answer the prompt regarding acceptance criteria and study details for an AI/ML device. The document primarily discusses the safety and efficacy of the contact lens solution through various pre-clinical and clinical studies for a traditional medical device submission.

As a result, I cannot extract the following information from the provided text:

• A table of acceptance criteria and reported device performance for an AI/ML device.
• Sample sizes and data provenance for a test set of an AI/ML device.
• Number and qualifications of experts for AI/ML ground truth.
• Adjudication method for an AI/ML test set.
• Results from a multi-reader multi-case (MRMC) comparative effectiveness study for AI improvement.
• Standalone performance for an algorithm.
• Type of ground truth used for an AI/ML device.
• Sample size for the training set of an AI/ML device.
• How ground truth for the training set was established for an AI/ML device.

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